DOI: https://doi.org/10.14710/jtsiskom.2021.13902

Sistem rekomendasi peminatan peserta didik baru pada kurikulum K-13 menggunakan metode profile matching, simple additive weighting, dan kombinasi keduanya

Recommender systems for specializing new students in the K-13 curriculum using the profile matching, SAW, and a combination of both

Master of Informatics, Universitas Islam Negeri Sunan Kalijaga Yogyakarta, Jl. Marsda Adisucipto Yogyakarta 55281, Indonesia

Received: 6 Sep 2020; Revised: 28 Dec 2020; Accepted: 26 Feb 2021; Available online: 20 Apr 2021; Published: 30 Apr 2021.
Open Access Copyright (c) 2021 The Authors. Published by Department of Computer Engineering, Universitas Diponegoro
Creative Commons License This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

How to cite (IEEE): M. E. Iswanto, M. U. Siregar, S. 'Uyun, and M. T. Nuruzzaman, "Sistem rekomendasi peminatan peserta didik baru pada kurikulum K-13 menggunakan metode profile matching, simple additive weighting, dan kombinasi keduanya," Jurnal Teknologi dan Sistem Komputer, vol. 9, no. 2, pp. 96-105, Apr. 2021. https://doi.org/10.14710/jtsiskom.2021.13902
Citation Format:
Abstract
The selection of students' interests based on the 2013 curriculum (K-13) is carried out before students start learning in class X. Accuracy in its determination is required to ensure that students learn according to their interests and talents. This study applies three DSS methods, namely profile matching, SAW, and a combination of both, to provide accurate recommendations for determining these students' interests. The three methods are compared using the same alternatives and criteria to find the most dominant method. The results of this study indicate that the application of SPK can assist PPDB activities with an accuracy of 79.2 %. In determining interest for students, the combination method is the most dominant, with an accuracy of 78 %. The application of DSS not only helps the specialization process to be faster but also accurate. This is indicated by only 6 out of 122 students who chose specialization based on the DSS recommendation getting a score below the KKM.
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Keywords: K-13 curriculum; DSS; profile matching; SAW; specialization
Funding: Universitas Islam Negeri Sunan Kalijaga Yogyakarta

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Section: Original Research Articles
Language : ID
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  1. Badan Pengembangan Sumber Daya Manusia Pendidikan dan Kebudayaan dan Penjamin Mutu Pendidikan, Pedoman peminatan peserta didik. Kementerian Pendidikan dan Kebudayaan Republik Indonesia, Jakarta, 2013
  2. A. Nurlina, “Pemahaman pemilihan peminatan akademik peserta didik kelas X SMAN 5 Tangerang tahun 2017-2018,” Jurnal Penelitian Bimbingan dan Konseling, vol. 3, no. 1, pp. 9–36, 2018, doi: 10.30870/jpbk.v3i1.3195
  3. A. Mutaqin, A. P. Wibawa, and U. Pujianto, “Model analisis pengambilan keputusan peminatan di SMA menggunakan metode SAW,” Prosiding Seminar Ilmu Komputer. dan Teknologi Informasi, vol. 1, no. 1, pp. 123–126, 2016
  4. K. Kusrini, Konsep dan aplikasi sistem pendukung keputusan, 1st ed. Yogyakarta: Andi Offset, 2007
  5. A. Silakhudin and J. Sutopo, “Implementasi metode profile matching untuk menentukan minat siswa Madrasah Aliyah Wahid Hasyim,” Undergraduate thesis, Universitas Teknologi Yogyakarta, Indonesia, 2019
  6. S. Susanti, D. A. Irawati, and R. Rismanto, “Sistem pendukung keputusan penjurusan pada siswa SMA,” Jurnal Informatika Polinema, vol. 4, no. 3, pp. 48–53, 2017. doi: 10.33795/jip.v3i4.43
  7. B. W. Sari, “Perbandingan metode profile matching dan simple additive weighting pada penentuan jurusan siswa kelas X SMAN 2 Ngaglik,” Jurnal Ilmiah DASI, vol. 16, pp. 16–22, 2015
  8. M. E. Iswanto, “Sistem pendukung keputusan pemilihan kepala asrama pondok pesantren Assalafie Babakan Ciwaringin Cirebon dengan metode Simple Additive Weighting (SAW) dan Profile Matching,” Undergraduate thesis, UIN Sunan Kalijaga, Indonesia, 2016
  9. I. Septiana, M. Irfan, A. Rialdy, and B. Subaeki, “Sistem pendukung keputusan penentu dosen penguji dan pembimbing tugas akhir menggunakan fuzzy multiple attribute decision making dengan simple additive weighting (studi kasus: Jurusan Teknik Informatika UIN SGD Bandung),” JOIN (Jurnal Online Informatika), vol. 1, pp. 43–50, 2016
  10. P. Parjito, “Implementasi metode profile matching dan simple additive weighting (saw) pada sistem pendukung keputusan menentukan varietas padi yang cocok untuk lahan pertanian,” Undergraduate thesis, Universitas Gadjah Mada, Indonesia, 2017
  11. R. E. Setyani and R. Saputra, “Flood-prone areas mapping at Semarang city by using Simple Additive Weighting method,” Procedia – Social and Behavioral Sciences, vol. 227, pp. 378–386, 2016. doi: 10.1016/j.sbspro.2016.06.089
  12. A. A. Mahardhika, R. Saptono, and R. Anggrainingsih, “Sistem klasifikasi feedback pelanggan dan rekomendasi solusi atas keluhan Di UPT Puskom UNS dengan algoritma Naïve Bayes Classifier dan Cosine Similarity,” Jurnal ITSMART, vol. 4, no. 1, pp. 36–42, 2015
  13. N. Kusumawardhany, Nurmasyah, and A. S. Wardani, “Penerapan metode AHP dan profile matching dalam penentuan jurusan SMA,” Jurnal Budi Luhur Information Technology, vol. 16, no. 2, pp. 35–41, 2019
  14. A. Verdian and A. Wantoro, “Komparasi metode profile matching dengan fuzzy profile matching pada pemilihan wakil kepala sekolah,” Jurnal Ilmiah Media Sisfo, vol. 13, no. 2, pp. 97–105, 2019. doi: 10.33998/mediasisfo.2019.13.2.652
  15. T. Wulandari and A. Susanto, “Deteksi tingkat risiko kehamilan dengan metode fuzzy mamdani dan simple additive weighting,” Jurnal Teknologi dan Sistem Komputer, vol. 6, no. 3, pp. 110–114, 2018. doi: 10.14710/jtsiskom.6.3.2018.110-114
  16. O. Kartikasari, N. Hidayat, and S. Suprapto, “Implementasi metode profile matching dengan weighted product pada seleksi staf operator komputer (Studi Kasus : UPTD Pendidikan Kecamatan Tambakboyo Kabupaten Tuban Jawa Timur),” Jurnal Pengembangan Teknologi Informasi dan Ilmu Komputer, vol. 1, no. 11, pp. 1331–1336, 2017
  17. Direktorat Pembinaan SMA, Panduan Penilaian Oleh Pendidik dan Satuan Pendidik Sekolah Menengah Atas. Ditjen Pendidikan Dasar dan Menengah, Kementerian Pendidikan dan Kebudayaan Republik Indonesia, Jakarta 2017
  18. P. Jayadi, A. S. Yazid, and M. Mustakim, “Bidikmisi scholarship selection in uin sunan kalijaga using profile matching method,” International Journal on Informatics Development, vol. 5, no. 2, pp. 6-11, 2016. doi: 10.14421/ijid.2016.05202

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